Technical Note: \cal Q-Learning
Machine Learning
MetaCost: a general method for making classifiers cost-sensitive
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning and making decisions when costs and probabilities are both unknown
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Segmentation-based modeling for advanced targeted marketing
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neuro-Dynamic Programming
AdaCost: Misclassification Cost-Sensitive Boosting
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Bootstrap Methods for the Cost-Sensitive Evaluation of Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
The foundations of cost-sensitive learning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Mining Plans for Customer-Class Transformation
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Cross channel optimized marketing by reinforcement learning
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning policies for sequential time and cost sensitive classification
UBDM '05 Proceedings of the 1st international workshop on Utility-based data mining
Extracting Actionable Knowledge from Decision Trees
IEEE Transactions on Knowledge and Data Engineering
Dynamic Catalog Mailing Policies
Management Science
Data acquisition and cost-effective predictive modeling: targeting offers for electronic commerce
Proceedings of the ninth international conference on Electronic commerce
Application of reinforcement learning to the game of Othello
Computers and Operations Research
Optimizing marketing planning and budgeting using Markov decision processes: an airline case study
IBM Journal of Research and Development - Business optimization
Data Mining and Knowledge Discovery
Data Mining and Knowledge Discovery
Reinforcement Learning with the Use of Costly Features
Recent Advances in Reinforcement Learning
Assigning discounts in a marketing campaign by using reinforcement learning and neural networks
Expert Systems with Applications: An International Journal
A new marketing strategy map for direct marketing
Knowledge-Based Systems
Optimizing debt collections using constrained reinforcement learning
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Model selection in markovian processes
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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Recently, there has been increasing interest in the issues of cost-sensitive learning and decision making in a variety of applications of data mining. A number of approaches have been developed that are effective at optimizing cost-sensitive decisions when each decision is considered in isolation. However, the issue of sequential decision making, with the goal of maximizing total benefits accrued over a period of time instead of immediate benefits, has rarely been addressed. In the present paper, we propose a novel approach to sequential decision making based on the reinforcement learning framework. Our approach attempts to learn decision rules that optimize a sequence of cost-sensitive decisions so as to maximize the total benefits accrued over time. We use the domain of targeted' marketing as a testbed for empirical evaluation of the proposed method. We conducted experiments using approximately two years of monthly promotion data derived from the well-known KDD Cup 1998 donation data set. The experimental results show that the proposed method for optimizing total accrued benefits out performs the usual targeted-marketing methodology of optimizing each promotion in isolation. We also analyze the behavior of the targeting rules that were obtained and discuss their appropriateness to the application domain.